scholarly journals Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
Dynamic Relationships ◽  
The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

scholarly journals Silent world under COVID-19 — a comprehensive impact analysis based on human mobility

2021 ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
The Impact ◽  
Travel Restriction

Abstract Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by COVID-19 pandemic. To better measure real-time decline of human mobility and changes of socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and places. Bi-directional causality between SI and the new COVID-19 cases was detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. Underdeveloped countries are more affected by the COVID-19 pandemic.

scholarly journals Impact of Financial Inclusion on Income Generation and Savings in Mountain Regions: A Case Study of Rural Households of Uttarakhand

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Prashant Kandari ◽  
Kusum Dobriyal ◽  
Uma Bahuguna
Keyword(s):  
Positive Impact ◽  
The State ◽  
Financial Inclusion ◽  
Income Generation ◽  
Mountain Regions ◽  
Economic Activities ◽  
Long Run ◽  
Economically Deprived ◽  
Caste Groups ◽  
The Impact

The major drive for financial inclusion started in the country mainly from year 2014 after the launch of Jan Dhan Yojana which focused mainly towards empowering dwellers of resource deprived and underdeveloped regions. Economic empowerment of such deprived sections is possible only when they are provided with ample opportunities for income generation in various economic activities. The prominent aim of financial inclusion is to facilitate residents of such deprived regions by reaching out to them and delivering them facilities so that they could identify and work on their capabilities to generate employment and income earning opportunities. The economically deprived and vulnerable population, through it, could be secured, from falling in the trap of poor informal level activities. These low-level informal activities are not suitable for them and for their overall development in the long run. Financial inclusion thus helps them in getting out of the poverty trap and hence acts as one of the important facility or an instrument which could help in a larger achievement of the goal of the development of residents of such deprived regions. Keeping these aspects in consideration the present study aims to understand the impact of financial inclusion on two important variables i.e. income generation and enhancement of savings among the residents of Mountain regions of the state which also represents the deprived and underdeveloped regions. The study was conducted in three mountain districts of the state and the results of the study shows that financial inclusion has helped in income generation in these regions but the impact of it has been different among different caste categories. Further the study shows that financial inclusion worked to enhance the income generation with larger benefits to households having higher levels of income. The study also depicts the positive impact of financial inclusion on savings but with noticeable variations in its impact on different caste groups.

Impact of Share Splits on Stock Returns: Evidences from India

2019 ◽  
Vol 23 (4) ◽  
pp. 432-441
Author(s):  
Bilal Ahmad Pandow ◽  
Khurshid Ahmad Butt
Keyword(s):  
Stock Returns ◽  
Impact Analysis ◽  
Positive Influence ◽  
Market Model ◽  
Sign Test ◽  
Rank Test ◽  
Long Run ◽  
Short Run ◽  
Study Methodology ◽  
The Impact

This article empirically examines the impact of stock splits on the price movements and returns of the scrips listed on the stock market in India. The study makes use of the standard event study methodology to measure the significance of unusual yield associated with the event. To calculate the returns, the study employs market model. Also, it uses parametric tests, such as t-statistic, and non-parametric test, such as Corrado Rank Test, Generalized Rank Test and Sign Test, to check the significance and robustness of abnormal return (AR), average AR, and cumulative average AR. Indisputably, the results are somewhat different from the evidences found in developed markets. Mostly in these countries, the event witnesses unusual optimistic yields. The results suggest that there is a positive AR adjacent to the effective day (ED) of the event in the short run. However, in the long run, negative ARs in the post-effective to ED+90 days window is witnessed. Further, the analysis also suggests that share splits do not have a positive influence on the share capital of the investors. The results are based on the 10-days event and 90-days estimation window and are the main limitation of the study. Hence, the windows can be both expanded and reduced to have a better holistic impact analysis of the share splits and stock returns of the selected firms.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals Human Mobility and Epidemic Evolution

2021 ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

Abstract The CoViD-19 pandemic ceased to be describable by a susceptible-infected-recovered (SIR) model when lockdowns were enforced. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 (Sudden Acute Respiratory Syndrome Coronavirus 2) in terms of individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

scholarly journals On the use of aggregated human mobility data to estimate the reproduction number

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

AbstractThe reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

scholarly journals ANALYSIS OF POTENTIAL EMPLOYMENT CONSEQUENCES ASSOCIATED WITH THE NOVEL CORONAVIRUS: CASE OF KUWAIT

2020 ◽  
Author(s):  
Dušana Alshatti Schmidt ◽  
Keyword(s):  
Labor Markets ◽  
International Institutions ◽  
Secondary Data ◽  
The Novel ◽  
Crucial Importance ◽  
Economic Activities ◽  
Long Run ◽  
Novel Coronavirus ◽  
The Impact ◽  
Future Growth

The novel coronavirus (COVID-19) outbreak has already left a mark on the economic activities and labor markets in both advanced and developing countries. While the impacts on the economy vary considerably, the oil dependent economies have been hit harder. Along with the impact of the pandemic disease, they have been contending with a major collapse in oil prices. Kuwait is the world’s seventh largest exporter of oil. Falling oil demand might affect the future growth of Kuwait’s economy in the long run, and if the crisis continues, possibility to provide employment opportunities will be challenged. The aim of this paper is to analyze potential pandemic’s impact on employment in Kuwait in comparison with the financial crisis from 2008-2009, what is of crucial importance for the businesses in the region to understand. The paper is based on a systematic review of the secondary data gathered by international institutions.

scholarly journals Control strategies against COVID-19 in China: Significance of effective testing in the long run

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253901
Author(s):  
Yatang Lin ◽  
Fangyuan Peng
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Detection Rate ◽  
Control Strategies ◽  
Human Mobility ◽  
Significant Growth ◽  
Mobility Data ◽  
Time Horizons ◽  
Long Run

The COVID-19 pandemic has become a long-term crisis that calls for long-term solutions. We combined an augmented SEIR simulation model with real-time human mobility data to decompose the effects of lockdown, travel bans and effective testing measures in the curtailment of COVID-19 spread in China over different time horizons. Our analysis reveals that the significant growth in the detection rate of infectious cases, thanks to the expansion in testing efficiency, were as effective as city lockdowns in explaining the reduction in new infections up to mid-March. However, as we extended the analysis to July, increasing the detection rate to at least 50% is the only reliable way to bring the spread under control.

scholarly journals Using Mobility Data to Understand and Forecast COVID19 Dynamics

2020 ◽  
Author(s):  
Lijing Wang ◽  
Xue Ben ◽  
Aniruddha Adiga ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Spatial Interaction ◽  
Human Mobility ◽  
State Level ◽  
Interaction Patterns ◽  
Disease Dynamics ◽  
Mobility Data ◽  
Spatio Temporal ◽  
The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

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